{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import matplotlib \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "from scipy.stats import skew \n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'],\n",
    "               test.loc[:, 'MSSubClass': 'SaleCondition']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000266BA3FA390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000266BA495080>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (12, 6)\n",
    "prices = pd.DataFrame({'price': train['SalePrice'],\n",
    "                       'log(price+1)':np.log1p(train['SalePrice'])})\n",
    "prices.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['SalePrice'] = np.log1p(train['SalePrice'])\n",
    "\n",
    "numeric_feats = all_data.dtypes[all_data.dtypes != 'object'].index \n",
    "# numeric_feats = all_data.dtypes[all_data.dtypes != 'object'].index\n",
    "\n",
    "skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna()))\n",
    "\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.75]\n",
    "skewed_feats = skewed_feats.index\n",
    "\n",
    "all_data[skewed_feats] = np.log1p(all_data[skewed_feats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.get_dummies(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.fillna(all_data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = all_data[:train.shape[0]]\n",
    "X_test = all_data[train.shape[0]:]\n",
    "y = train.SalePrice "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV \n",
    "from sklearn.model_selection import cross_val_score \n",
    "\n",
    "def rmse_cv(model):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X_train, y, scoring='neg_mean_squared_error', cv=5))\n",
    "    return rmse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ridge = Ridge()\n",
    "\n",
    "alphas = [0.05, 0.1, 0.3, 1, 3, 10, 15, 30, 50, 75]\n",
    "cv_ridge = [rmse_cv(Ridge(alpha=alpha)).mean() for alpha in alphas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'rmse')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_ridge = pd.Series(cv_ridge, index=alphas)\n",
    "cv_ridge.plot(title = 'Validation')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1273373466867074"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_ridge.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12314421090977441"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lasso = LassoCV(alphas=[1, 0.1, 0.001, 0.0005]).fit(X_train, y)\n",
    "rmse_cv(model_lasso).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 110 variables and eliminated the other 178\n"
     ]
    }
   ],
   "source": [
    "coef = pd.Series(model_lasso.coef_, index=X_train.columns)\n",
    "print('Lasso picked '+ str(sum(coef!=0)) + ' variables and eliminated the other ' + str(sum(coef==0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Coefficients in the lasso model')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the most coefficients \n",
    "imp_coef = pd.concat([coef.sort_values().head(10),\n",
    "                     coef.sort_values().tail(10)])\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = (8,10)\n",
    "imp_coef.plot(kind='barh')\n",
    "plt.title('Coefficients in the lasso model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a24bd3e10>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = pd.DataFrame({'preds': model_lasso.predict(X_train), 'true':y})\n",
    "preds['residuals'] = preds['true'] - preds['preds']\n",
    "preds.plot(x='preds', y='residuals', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.69490559, 11.92823243, 12.10183291, ..., 12.03779924,\n",
       "       11.68640318, 12.33887195])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = model_lasso.predict(X_test)\n",
    "solution = pd.DataFrame({'id':test.Id, 'SalePrice':np.expm1(preds)})\n",
    "solution.to_csv('linear_sol.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb \n",
    "\n",
    "dtrain = xgb.DMatrix(X_train, label=y)\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "\n",
    "params = {'max_depth':1, 'eta':0.1}\n",
    "model = xgb.cv(params, dtrain, num_boost_round=500, early_stopping_rounds=180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a2509d860>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.loc[30:, ['test-rmse-mean', 'train-rmse-mean']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:27:58] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=360,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb = xgb.XGBRegressor(n_estimators=360, max_depth=2, learning_rate=0.1)\n",
    "model_xgb.fit(X_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a25117860>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_preds = np.expm1(model_xgb.predict(X_test))\n",
    "lasso_preds = np.expm1(model_lasso.predict(X_test))\n",
    "\n",
    "predictions = pd.DataFrame({'xgb':xgb_preds, 'lasso':lasso_preds})\n",
    "predictions.plot(x='xgb', y='lasso', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = 0.7*lasso_preds + 0.3*xgb_preds\n",
    "solution = pd.DataFrame({'id':test.Id, 'SalePrice':preds})\n",
    "solution.to_csv('ridge_sol2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
